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RISS 인기검색어
- 검색키워드
- 저자 : Tomonari Furukawa (검색결과 6 건)
- 무료
- 기관 내 무료
- 유료
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A Probabilistic Model-adaptive Approach for Tracking of Motion with Heightened Uncertainty
J. Josiah Steckenrider,Tomonari Furukawa 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.10
This paper presents an approach for state tracking in scenarios where motion is highly uncertain. The proposed approach improves on traditional Kalman filters by integrating model parametric uncertainty in deriving state covariances for prediction at each time step. A model correction stage then continuously adjusts the mean and variance of state matrix elements based on the observation-corrected state, compensating for an initially inadequate system model. The symbiotic relationship between state tracking and motion model correction is leveraged toperform both tasks simultaneously in-the-loop. In a representative dynamic example, simulated experiments were performed and analyzed statistically for varying combinations of sensor and model uncertainty. For low model variance, traditional Kalman filters generally perform estimation better due to over-confidence with regards to model parameters. However, the proposed approach increasingly outperforms both traditional and adaptive Kalman filters in estimation when model and input uncertainty is appreciable. The motion model updating approach formulated here tends to improve parameter estimates over the course of state tracking, thus validating the symbiotic process. The robotics applications of this simultaneous estimation and modeling framework extend from target state tracking to self-state estimation, while broader signal processing applications can be readily extracted.
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Joon-Seong Lee,Ho-Jeong Lee,Tomonari Furukawa 한국지능시스템학회 2009 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.9 No.3
Up to now, a number of models have been proposed and discussed to describe a wide range of inelastic behaviors of materials. The fatal problem of using such models is however the existence of model errors, and the problem remains inevitably as far as a material model is written explicitly. In this paper, the authors define the implicit constitutive model and propose an implicit viscoplastic constitutive model using neural networks. In their modeling, inelastic material behaviors are generalized in a state space representation and the state space form is constructed by a neural network using input-output data sets. A technique to extract the input-output data from experimental data is also described. The proposed model was first generated from pseudo-experimental data created by one of the widely used constitutive models and was found to replace the model well. Then, having been tested with the actual experimental data, the proposed model resulted in a negligible amount of model errors indicating its superiority to all the existing explicit models in accuracy.
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Formulation of the Neural Network for Implicit Constitutive Model (Ⅱ)
Joon-Seong Lee,Eun-Chul Lee,Tomonari Furukawa 한국지능시스템학회 2008 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.8 No.4
In this paper, two neural networks as a material model, which are based on the state-space method, have been proposed. One outputs the rates of inelastic strain and material internal variables whereas the outputs of the other are the next state of the inelastic strain and material internal variables. Both the neural networks were trained using input-output data generated from Chaboche's model and successfully converged. The former neural network could reproduce the original stress-strain curve. The neural network also demonstrated its ability of interpolation by generating untrained curve. It was also found that the neural network can extrapolate in close proximity to the training data.
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Lee, Joon-Seong,Lee, Ho-Jeong,Furukawa, Tomonari Korean Institute of Intelligent Systems 2009 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.9 No.3
Up to now, a number of models have been proposed and discussed to describe a wide range of inelastic behaviors of materials. The fatal problem of using such models is however the existence of model errors, and the problem remains inevitably as far as a material model is written explicitly. In this paper, the authors define the implicit constitutive model and propose an implicit viscoplastic constitutive model using neural networks. In their modeling, inelastic material behaviors are generalized in a state space representation and the state space form is constructed by a neural network using input-output data sets. A technique to extract the input-output data from experimental data is also described. The proposed model was first generated from pseudo-experimental data created by one of the widely used constitutive models and was found to replace the model well. Then, having been tested with the actual experimental data, the proposed model resulted in a negligible amount of model errors indicating its superiority to all the existing explicit models in accuracy.
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Lee, Joon-Seong,Lee, Eun-Chul,Furukawa, Tomonari Korean Institute of Intelligent Systems 2008 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.8 No.4
In this paper, two neural networks as a material model, which are based on the state-space method, have been proposed. One outputs the rates of inelastic strain and material internal variables whereas the outputs of the other are the next state of the inelastic strain and material internal variables. Both the neural networks were trained using input-output data generated from Chaboche's model and successfully converged. The former neural network could reproduce the original stress-strain curve. The neural network also demonstrated its ability of interpolation by generating untrained curve. It was also found that the neural network can extrapolate in close proximity to the training data.
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Inelastic Constitutive Modeling for Viscoplastcity Using Neural Networks
Lee, Joon-Seong,Lee, Yang-Chang,Furukawa, Tomonari Korean Institute of Intelligent Systems 2005 한국지능시스템학회논문지 Vol.15 No.2
Up to now, a number of models have been proposed and discussed to describe a wide range of inelastic behaviors of materials. The fetal problem of using such models is however the existence of model errors, and the problem remains inevitably as far as a material model is written explicitly. In this paper, the authors define the implicit constitutive model and propose an implicit viscoplastic constitutive model using neural networks. In their modeling, inelastic material behaviors are generalized in a state space representation and the state space form is constructed by a neural network using input output data sets. A technique to extract the input-output data from experimental data is also described. The proposed model was first generated from pseudo-experimental data created by one of the widely used constitutive models and was found to replace the model well. Then, having been tested with the actual experimental data, the proposed model resulted in a negligible amount of model errors indicating its superiority to all the existing explicit models in accuracy.
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